﻿* Encoding: UTF-8.
dataset close all.

GET DATA
  /TYPE=XLSX
  /FILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Survey\Copy of Raw data.xlsx'
  /SHEET=name 'Data'
  /CELLRANGE=FULL
  /READNAMES=ON
  /DATATYPEMIN PERCENTAGE=95.0
  /HIDDEN IGNORE=YES.
EXECUTE.
DATASET NAME rawdata WINDOW=FRONT.

ren var Q3_region = region.

recode region ("Languedoc-Roussillon, Provence-Alpes-Côte d'Azur, Corse" = "Languedoc-Roussillon, Provence-Alpes-Côte dAzur, Corse")
    ("Valle d'Aosta, Liguria, Lombardia, Piemonte" = "Valle dAosta, Liguria, Lombardia, Piemonte").
exe.
fre region.

compute regioncode = -99.
if (region = 'Ostösterreich') regioncode = 101.
if (region = 'Südösterreich') regioncode = 102.
if (region = 'Westösterreich') regioncode = 103.
if (region = 'East Midlands') regioncode = 201.
if (region = 'East of England') regioncode = 202.
if (region = 'London') regioncode = 203.
if (region = 'North East') regioncode = 204.
if (region = 'North West') regioncode = 205.
if (region = 'Northern Ireland') regioncode = 206.
if (region = 'Scotland') regioncode = 207.
if (region = 'South East') regioncode = 208.
if (region = 'South West') regioncode = 209.
if (region = 'Wales') regioncode = 210.
if (region = 'West Midlands') regioncode = 211.
if (region = 'Yorkshire and the Humber') regioncode = 212.
if (region = 'Brussels') regioncode = 301.
if (region = 'Flanders') regioncode = 302.
if (region = 'Wallonia') regioncode = 303.
if (region = 'Северна и Източна България') regioncode = 401.
if (region = 'Югозападна и Южна Централна България') regioncode = 402.
if (region = 'Jihovychod') regioncode = 501.
if (region = 'Jihozápad') regioncode = 502.
if (region = 'Moravskozlezsco') regioncode = 503.
if (region = 'Praha') regioncode = 504.
if (region = 'Severovychod') regioncode = 505.
if (region = 'Severozápad') regioncode = 506.
if (region = 'Střední Čechy') regioncode = 507.
if (region = 'Stredni Morava') regioncode = 508.
if (region = 'Region Hovedstaden') regioncode = 601.
if (region = 'Region Midtjylland') regioncode = 602.
if (region = 'Region Nordjylland') regioncode = 603.
if (region = 'Region Sjælland') regioncode = 604.
if (region = 'Region Syddanmark') regioncode = 605.
if (region = 'Ahvenanmaa') regioncode = 701.
if (region = 'Etelä-Suomi') regioncode = 702.
if (region = 'Helsinki-Uusimaa') regioncode = 703.
if (region = 'Länsi -Suomi') regioncode = 704.
if (region = 'Pohjois- tai Itä-Suomi') regioncode = 705.
if (region = 'Aquitaine, Midi-Pyrénées, Limousin') regioncode = 801.
if (region = 'Champagne-Ardenne, Picardie, Haute-Normandie, Centre, Basse-Normandie, Bourgogne') regioncode = 802.
if (region = 'Guadeloupe, Martinique, Guinée Française, La Réunion, Mayotte') regioncode = 803.
if (region = 'Ile de France') regioncode = 804.
if (region = 'Languedoc-Roussillon, Provence-Alpes-Côte dAzur, Corse') regioncode = 805.
if (region = 'Lorraine, Alsace, Franche-Compté') regioncode = 806.
if (region = 'Nord-Pas-de-Calais') regioncode = 807.
if (region = 'Pays de la Loire, Bretagne, Poitou-Charentes') regioncode = 808.
if (region = 'Rhône-Alpes, Auvergne') regioncode = 809.
if (region = 'Baden-Württemberg') regioncode = 901.
if (region = 'Bayern') regioncode = 902.
if (region = 'Berlin') regioncode = 903.
if (region = 'Brandenburg') regioncode = 904.
if (region = 'Bremen') regioncode = 905.
if (region = 'Hamburg') regioncode = 906.
if (region = 'Hessen') regioncode = 907.
if (region = 'Mecklenburg-Vorpommern') regioncode = 908.
if (region = 'Niedersachsen') regioncode = 909.
if (region = 'Nordrhein-Westfalen') regioncode = 910.
if (region = 'Rheinland-Pfalz') regioncode = 911.
if (region = 'Saarland') regioncode = 912.
if (region = 'Sachsen') regioncode = 913.
if (region = 'Sachsen-Anhalt') regioncode = 914.
if (region = 'Schleswig-Holstein') regioncode = 915.
if (region = 'Thüringen') regioncode = 916.
if (region = 'Ανατολική Μακεδονία και Θράκη') regioncode = 1001.
if (region = 'Αττική') regioncode = 1002.
if (region = 'Δυτική Μακεδονία') regioncode = 1003.
if (region = 'Ήπειρος') regioncode = 1004.
if (region = 'Θεσσαλία') regioncode = 1005.
if (region = 'Ιόνια νησιά') regioncode = 1006.
if (region = 'Κεντρική Μακεδονία') regioncode = 1007.
if (region = 'Κρήτη') regioncode = 1008.
if (region = 'Περιφέρεια Βορείου Αιγαίου') regioncode = 1009.
if (region = 'Περιφέρεια Δυτικής Ελλάδας') regioncode = 1010.
if (region = 'Περιφέρεια Νοτίου Αιγαίου') regioncode = 1011.
if (region = 'Περιφέρεια Πελοποννήσου') regioncode = 1012.
if (region = 'Στερεά Ελλάδα') regioncode = 1013.
if (region = 'Border, Midlands and Western') regioncode = 1101.
if (region = 'Southern and Eastern') regioncode = 1102.
if (region = 'Abruzzo, Puglia, Basilicata, Calabria, Campania, Molise') regioncode = 1201.
if (region = 'Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige/Südtirol, Veneto') regioncode = 1202.
if (region = 'Lazio, Marche, Toscana, Umbria') regioncode = 1203.
if (region = 'Sardegna, Sicilia') regioncode = 1204.
if (region = 'Valle dAosta, Liguria, Lombardia, Piemonte') regioncode = 1205.
if (region = 'Noord (Groningen, Friesland of Drenthe)') regioncode = 1301.
if (region = 'Oost (Overijssel, Gelderland, Flevoland)') regioncode = 1302.
if (region = 'West (Utrecht, Noord-Holland, Zuid-Holland, Zeeland)') regioncode = 1303.
if (region = 'Zuid (Noord-Brabant, Limburg)') regioncode = 1304.
if (region = '_ód_, Mazowsze') regioncode = 1401.
if (region = 'Dolny _l_sk, Opolszczyzna') regioncode = 1402.
if (region = 'Kujawsko-Pomorskie, Warmia i Mazury, Pomorze') regioncode = 1403.
if (region = 'Lubelszczyzna, Podkarpacie, _wi_tokrzyskie, Podlaskie') regioncode = 1404.
if (region = 'Ma_opolska, Górny _l_sk') regioncode = 1405.
if (region = 'Wielkopolska, Zachodnie Pomorze, Lubuskie') regioncode = 1406.
if (region = 'Andalucía, Región de Murcia, Ceuta, Melilla') regioncode = 1501.
if (region = 'Canarias') regioncode = 1502.
if (region = 'Castilla y León, Castilla-La Mancha, Extremadura') regioncode = 1503.
if (region = 'Cataluña, Comunidad Valenciana, Islas Baleares') regioncode = 1504.
if (region = 'Comunidad de Madrid') regioncode = 1505.
if (region = 'Galicia, Principado de Asturias, Cantabria') regioncode = 1506.
if (region = 'País Vasco, Navarra, La Rioja, Aragón') regioncode = 1507.


*cro region by regioncode.
*fre regioncode.

**delete respondents without region.
select if regioncode ne -99.
*fre regioncode.

*give country codes.
compute countrycode=-99.
if (country = 'Austria') countrycode = 1.
if (country = 'Belgium(FR)') countrycode = 3.
if (country = 'Belgium(NL)') countrycode = 3.
if (country = 'Bulgaria') countrycode = 4.
if (country = 'Czech Republic') countrycode = 5.
if (country = 'Denmark') countrycode = 6.
if (country = 'Finland') countrycode = 7.
if (country = 'France') countrycode = 8.
if (country = 'Germany') countrycode = 9.
if (country = 'Greece') countrycode = 10.
if (country = 'Ireland') countrycode = 11.
if (country = 'Italy') countrycode = 12.
if (country = 'Netherlands') countrycode = 13.
if (country = 'Poland') countrycode = 14.
if (country = 'Spain') countrycode = 15.
if (country = 'UK') countrycode = 2.
val lab countrycode  1 "AT" 2 "UK" 3 "BE" 4 "BG" 5 "CZ" 6 "DK" 7 "FI" 8 "FR" 9 "DE" 10 "EL" 11 "IE" 12 "IT" 13 "NL" 14 "PL" 15 "ES".
*fre countrycode.

*match regional variables.

GET
  FILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Regional '+
    'data\Regional data.sav'.
DATASET NAME regional .


DATASET ACTIVATE rawdata.
SORT CASES BY regioncode.
DATASET ACTIVATE regional.
SORT CASES BY regioncode.
DATASET ACTIVATE rawdata.
MATCH FILES /FILE=*
  /TABLE='regional'
  /RENAME (Country Countrycode Region = d0 d1 d2) 
  /BY regioncode
  /DROP= d0 d1 d2.
EXECUTE.

dataset close regional.
*match national variables.
fre country.

GET
  FILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Regional '+
    'data\National data.sav'.
DATASET NAME national .

SORT CASES BY countrycode.
DATASET ACTIVATE national.
SORT CASES BY countrycode.
DATASET ACTIVATE rawdata.
MATCH FILES /FILE=*
  /TABLE='national'
  /RENAME (GEO nutscode = d0 d1) 
  /BY countrycode
  /DROP= d0 d1.
EXECUTE.

rename variables (EQI.2010 EQI.2013 EQI.2017 EQI.2021 = EQI_2010 EQI_2013 EQI_2017 EQI_2021). 
rename variables (EQI.2010_country EQI.2013_country EQI.2017_country EQI.2021_country = EQI_2010_country EQI_2013_country EQI_2017_country EQI_2021_country). 

SAVE OUTFILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Raw data with regional variables.sav'
  /COMPRESSED.

SAVE TRANSLATE OUTFILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Raw data with regional variables.dta'
  /TYPE=STATA
  /VERSION=14
  /EDITION=SE
  /MAP
  /REPLACE.

*************************************************************************************************************.
**prepare survy data. 

dataset close all.

GET
  FILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Raw data with regional variables.sav'.
DATASET NAME survey.

*Dependent variables.
***recode DMP and referenda.
    
recode q28 ('Strongly disagree' = 1) ('Disagree' = 2) ('Neutral' = 3) ('Agree' = 4) ('Strongly agree' = 5) (else=sysmis) into DMP.
recode q30_1 ('Strongly disagree' = 1) ('Disagree' = 2) ('Neutral' = 3) ('Agree' = 4) ('Strongly agree' = 5) (else=sysmis) into REF.
val lab DMP REF 1 'Strongly disagree' 2 'Disagree' 3 'Neutral' 4 'Agree' 5 'Strongly agree'.
    fre q28 DMP q30_1 REF.

**Independent variables - individual level.

*Political deprivation.
* Dissatisfaction with democracy (Q22) or government (Q24).
*fre Q22_1 Q24_1.
alter type Q22_1 Q24_1 (F3.0).
recode Q22_1 Q24_1  (-999=sysmis) (else=copy) into sat_dem sat_gov.
*fre sat_dem sat_gov.

*Distrust in representative institutions (scale of the three first items - Q23).
fre Q23_1 Q23_2 Q23_3.

recode Q23_1 Q23_2 Q23_3 ('2' = 2) ('3' = 3) ('4' = 4) ('High trust (5)' = 5) ('No trust at all (1)' = 1) (else=sysmis) into trust_parliament trust_politicians trust_parties.
fre trust_parliament trust_politicians trust_parties.

REL /VAR = trust_parliament trust_politicians trust_parties   /SCALE('ALL VARIABLES') ALL .
*0.91.

compute trust_rep = mean.2(trust_parliament, trust_politicians, trust_parties).
*fre trust_rep.

*Socio-economic deprivation
*Unemployed status (Q7).
fre Q7_EmploymentStatus.

recode Q7_EmploymentStatus ('Doing housework, looking after children or other persons' = 4)  ('In education' = 4) ('In paid work' = 1) ('Other' = 4) ('Permanently sick or disabled' = 4) ('Retired' = 4) ('Unemployed and active seeker' = 2)
('Unemployed and inactive seeker' = 3) (else=sysmis) into employment2.
recode employment2 (3=2) (4=3) (else=copy) into employment.
value labels employment 1 'paid work' 2  'unemployed (active +inactive seeker)' 3 'other'.
value labels employment2 1 'paid work' 2  'unemployed (active seeker)' 3 'unemployed (inactive seeker)' 4 'other'.
*fre Q7_EmploymentStatus employment employment2.

*Income insecurity feeling (Q9).
*fre Q9_SubjectiveIncome.
recode Q9_SubjectiveIncome ('Coping on present income' = 3)  ('Difficult on present income' = 2) ('Living comfortably on present income' = 4) ('Living very comfortably on present income' = 5) ('Very difficult on present income' = 1) (else=sysmis)
into income.
value labels income 1 "Very Difficult" 2 "Difficult" 3 "Coping" 4 "Comfortable" 5 "Very comfortable".
*cro Q9_SubjectiveIncome by income.

*Control variables.
*Urbanity (Q6).
*fre Q6_UrbanRural.
recode Q6_UrbanRural ('A big city' = 5) ('A country village' = 2) ('A farm or home in the country' = 1) ('A town or small city' = 3) ('The suburbs or outskirts of a big city' = 4)  (else=sysmis)   into urban.
 value labels urban 1 "country home" 2 "village" 3 "town" 4 "suburbs of city" 5 "big city".
recode urban (1,2=1) (3=2) (4,5=3) into urban_group.
value labels urban_group 1 'countryside' 2 'town' 3 'big city'.
*fre urban_group.
*fre Q6_UrbanRural urban.
*Sex (Q1), age , education (Q4).
*fre Q1_Gender Q2_Age Q4_Education. 
recode Q1_Gender ('Female' = 0) ('Male' = 1) (else=sysmis)  into male.
*fre male.

*age.
 recode Q2_Age ('18-24 years old' = 1) ('25-34 years old' = 2) ('35-44 years old' = 3) ('45-54 years old' = 4) ('55-64 years old' = 5) ('65-74 years old' = 6) ('75 years old or more' = 6)
into age.
 value labels age 1 "18-24 years" 2 "25-34 years" 3 "35-44 years" 4 "45-54 years" 5 "55-64 years" 6 "65+".
*fre Q2_Age age.

*education.
recode Q4_education ('Bachelor or equivalent' = 7) ('Doctoral or equivalent' = 9) ('Early childhood education / no education' = 1) ('Lower secondary education' = 3)
     ('Master or equivalent' = 8) ('Post-secondary non-tertiary education' = 5) ('Primary education' = 2) ('Short-cycle tertiary education' = 6) ('Upper secondary education' = 4) into education. 
value labels education 1 "None" 2 "Primary" 3 "Lower Secondary" 4 "Upper Secondary" 5 "Vocational" 6 "Short-cycle Tertiary" 7 "Bachelor" 8 "Master" 9 "Doctorate".
*fre Q4_education education.
*cro Q4_education by education.
recode education (1 thru 3 = 1) (4,5=2) (6 thru 9 = 3) into edu_group.
value labels edu_group 1 'lower educated' 2 'middle educated' 3 'higher educated'.
*fre edu_group.
*means DMP REF by education.

*Political interest (Q18) and efficacy (Q19), LRSP (Q20).
*fre Q18_PoliticalInterest Q19_PoliticalEfficacy  Q20_LR_Placement.

recode Q18_PoliticalInterest  ('Not at all interested' = 1) ('Not very interested' = 2) ('Somewhat interested' = 3) ('Very interested' = 4) (else=sysmis)  into interest.
value labels interest 1 "not at all interested" 2 "not very interested" 3 "somewhat interested" 4 "very interested".
*cro Q18_PoliticalInterest  by interest.

recode Q19_PoliticalEfficacy ('Strongly disagree' = 1) ('Disagree' = 2) ('Agree' = 3) ('Strongly agree' = 4) (else=sysmis) into efficacy_rec .
value labels efficacy_rec 1 "Strongly disagree" 2 "Disagree" 3 "Agree" 4 "Strongly agree".
recode efficacy_rec (4=1) (3=2) (2=3) (1=4) into efficacy.
value labels efficacy 1 "Strongly agree" 2 "Agree" 3 "Disagree" 4 "Strongly disagree".

*cro efficacy by Q19_PoliticalEfficacy.

alter type Q20_LR_Placement (F3.0).
recode Q20_LR_Placement  (-999=sysmis) (else=copy) into lrsp.
*cro Q20_LR_Placement by lrsp.
*lots of missings.

***********************************************.
*added for RR.
**20 Feb 2025.
**************.

*participation.
compute participation = -99.
compute petition = -99.
compute memberparty = -99.
compute demonstration = -99.
compute campaign = -99.
compute contactpol = -99.

if (Q31_1 eq 'Oui' OR Q31_1 eq 'yes' OR Q31_1 eq 'Yes') petition = 1 .
if (Q31_1 eq 'Non' OR Q31_1 eq 'no' OR Q31_1 eq 'No') petition = 0 .
if (Q31_2 eq 'Oui' OR Q31_2 eq 'yes' OR Q31_2 eq 'Yes') memberparty = 1 .
if (Q31_2 eq 'Non' OR Q31_2 eq 'no' OR Q31_2 eq 'No') memberparty = 0 .
if (Q31_3 eq 'Oui' OR Q31_3 eq 'yes' OR Q31_3 eq 'Yes') demonstration = 1 .
if (Q31_3 eq 'Non' OR Q31_3 eq 'no' OR Q31_3 eq 'No') demonstration = 0 .
if (Q31_4 eq 'Oui' OR Q31_4 eq 'yes' OR Q31_4 eq 'Yes') campaign = 1 .
if (Q31_4 eq 'Non' OR Q31_4 eq 'no' OR Q31_4 eq 'No') campaign = 0 .
if (Q31_5 eq 'Oui' OR Q31_5 eq 'yes' OR Q31_5 eq 'Yes') contactpol = 1 .
if (Q31_5 eq 'Non' OR Q31_5 eq 'no' OR Q31_5 eq 'No') contactpol = 0 .
missing values  petition memberparty demonstration campaign contactpol (-99).

*fre petition memberparty demonstration campaign contactpol.

compute participation = sum.5(petition, memberparty, demonstration, campaign, contactpol).
recode participation (0=0) (1 thru highest=1) (else=sysmis)into par_dich.
fre participation par_dich.

***country level.

*QOG.
*fre EQI_2021.
*change. 
compute EQI_change21_17 = EQI_2021 - EQI_2017.
compute EQI_change21_13 = EQI_2021 - EQI_2013.
compute EQI_change21_10 = EQI_2021 - EQI_2010.
*fre EQI_change21_17 EQI_change21_13 EQI_change21_10.
*compared to country mean.
compute EQI_rel21 = EQI_2021 - EQI_2021_country.
*fre EQI_rel21.
*compared to country mean.
compute EQI_country_change17 = EQI_2021_country - EQI_2017_country.
compute EQI_relchange21_17 = EQI_change17  - EQI_country_change17.
*fre EQI_relchange21_17.


*unemployment.
compute unemrate_change21_20 = unemrate_2021 - unemrate_2020.
compute unemrate_change21_19 = unemrate_2021 - unemrate_2019.
compute unemrate_change21_16 = unemrate_2021 - unemrate_2016.

*compared to country mean.
compute unemrate_rel21 = unemrate_2021 - unemratecountry_2021.

*povertyrisk.
*poverty_2021.

compute poverty_change21_20 = poverty_2021 - poverty_2020.
compute poverty_change21_19 = poverty_2021 - poverty_2019.
compute poverty_change21_16 = poverty_2021 - poverty_2016.
compute poverty_change21_11 = poverty_2021 - poverty_2011.

*compared to country mean.
compute poverty_rel21 = poverty_2021 - povertycountry_2021.

*gdp.
compute gdp_change21_20 = gdp_2021 - gdp_2020.
compute gdp_change21_19 = gdp_2021 - gdp_2019.
compute gdp_change21_16 = gdp_2021 - gdp_2016.
compute gdp_change21_11 = gdp_2021 - gdp_2011.

*compared to country mean.
compute gdp_rel21 = gdp_2021 - gdpcountry_2021.

**gdp /1000.

*for 2020 / 2021 (the ones we use), divide by 1000.
rename variables (gdp_2020 gdp_2021 = gdp_2020_10000 gdp_2021_10000).
compute gdp_2020 = gdp_2020_10000/10000.
compute gdp_2021 = gdp_2021_10000/10000.
*fre gdp_2021.

*delete missing values.
select if PoorQual eq 0.
fre PoorQual.

*overview variables.
*DMP REF
 
*individual.
*employment employment1 income.
*sat_dem sat_gov trust_parliament trust_politicians trust_parties trust_rep.
*control.
*urban urban_group male age education edu_group interest efficacy lrsp.

*regional level.
* EQI_change21_17 EQI_change21_13 EQI_change21_10 EQI_rel21 EQI_relchange21_17
* capitalregion.
* unemrate_2021 unemrate_change21_20 unemrate_change21_19 unemrate_change21_16  unemrate_rel21 
*poverty_2021 poverty_change21_20 poverty_change21_19 poverty_change21_16 poverty_change21_11 poverty_rel21 
*gdp_2021 gdp_change21_20 gdp_change21_19  gdp_change21_16 gdp_change21_11 gdp_rel21
*country level.

*select to have valid score on both DVs (for comparability).
fre dmp ref.
select if not missing (dmp).
select if not missing (ref).

fre income.

compute sub_income = income.
fre sub_income.
fre capitalregion.
compute int_capregion = capitalregion.
fre int_capregion.
fre interest.

rename variables (efficacy = efficacy_labels).
compute efficacy=efficacy_labels.
fre efficacy efficacy_labels.
rename variables (interest = interest_labels).
compute interest=interest_labels.
fre interest interest_labels.


SAVE OUTFILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Data analysis.sav'
  /COMPRESSED.

SAVE TRANSLATE OUTFILE='C:\Users\tsipma\surfdrive\Wetenschappelijke artikelen\Emilien-David-Take\Data\Data analysis.dta'
  /TYPE=STATA
  /VERSION=14
  /EDITION=SE
  /MAP
  /REPLACE.

**descriptives (appendix 4).
cor unemrate_2021 gdp_2021 eqi_2021 capitalregion.

means unemrate_2021 by countrycode.
means gdp_2021 by countrycode.
means eqi_2021 by countrycode.
means ref by countrycode.
means dmp by countrycode.

compute di = mean(dmp,ref).
fre di.
means di by countrycode.
means di by regioncode.
means dmp by countrycode by regionEN .
means ref by countrycode by regionEN .
means unemrate_2021 by coutrycode.


cor gdp_2021 sub_income trust_rep efficacy interest sat_dem.

cor trust_rep sat_dem.

DATASET DECLARE region.
AGGREGATE
  /OUTFILE='region'
  /BREAK=regioncode
    /unemrate_2021_mean=MEAN(unemrate_2021) 
  /gdp_2021_mean=MEAN(gdp_2021) 
    /EQI_2021_mean=MEAN(EQI_2021)
  /N_BREAK=N.

cor unemrate_2021_mean gdp_2021_mean eqi_2021_mean.



DATASET ACTIVATE survey.
DATASET DECLARE region.
SORT CASES BY regioncode.
AGGREGATE
  /OUTFILE='region'
  /PRESORTED
  /BREAK=regioncode
  /poverty_2021_mean=MEAN(poverty_2021) 
  /EQI_2021_mean=MEAN(EQI_2021) 
  /capitalregion_mean=MEAN(capitalregion) 
  /unemrate_2021_mean=MEAN(unemrate_2021) 
  /unemrate_change21_20_mean=MEAN(unemrate_change21_20) 
  /unemrate_change21_19_mean=MEAN(unemrate_change21_19) 
  /unemrate_change21_16_mean=MEAN(unemrate_change21_16) 
  /unemrate_rel21_mean=MEAN(unemrate_rel21) 
  /poverty_change21_20_mean=MEAN(poverty_change21_20) 
  /poverty_change21_19_mean=MEAN(poverty_change21_19) 
  /poverty_change21_16_mean=MEAN(poverty_change21_16) 
  /poverty_change21_11_mean=MEAN(poverty_change21_11) 
  /poverty_rel21_mean=MEAN(poverty_rel21) 
  /gdp_change21_20_mean=MEAN(gdp_change21_20) 
  /gdp_change21_19_mean=MEAN(gdp_change21_19) 
  /gdp_change21_16_mean=MEAN(gdp_change21_16) 
  /gdp_change21_11_mean=MEAN(gdp_change21_11) 
  /gdp_rel21_mean=MEAN(gdp_rel21) 
  /gdp_2020_mean=MEAN(gdp_2020) 
  /gdp_2021_mean=MEAN(gdp_2021).

dataset activate region.
cor gdp_2021_mean unemrate_2021_mean poverty_2021_mean eqi_2021_mean capitalregion_mean.
cor gdp_2021_mean gdp_change21_20_mean gdp_change21_19_mean gdp_change21_16_mean gdp_change21_11_mean.



